In medical and biological research and in the analysis and detection of diseases many findings are based on analyzing cells. For the analysis of large quantities of cell, frequently flow cytometers are used. In a flow cytometer cells are conducted through a channel in a solution with high speed. These cells emit an optical signal, for example triggered by a light source, like e.g. a laser. This optical signal is detected by the flow cytometer and enables a determination of the characteristics of the cells in the solution, like e.g. the number of certain cell types or their size and other characteristics.
A further development of flow cytometry is based on the principle of the spatially modulated fluorescence (RMF; Räumlich Modulierte Fluoreszenz). In conventional flow cytometers the channel in which the solution with the cells is guided past the sensor is narrowed so that the cells pass the sensor individually. Here, highly precise optics are required for the detector and the laser for exciting and detecting the individual cells which require a complex setup and high space requirement of the flow cytometer. Flow cytometers which are based on the principle of spatially modulated frequencies may, however, simultaneously support the flow of several cells, for example in case of a lower complexity of the optical setup. The cells in the solution in the channel are excited simultaneously, for example by a laser, and the emitted light is influenced by a spatial filter, like a filter mask. The spatial filter provides for the sensor to detect a signal based on the filter for each excited cell. By an analysis of the signal which may be superposed by other cells, the cytometer may determine a number, speed and other characteristics of the cells in the solution. The quality of detection of the cells and cell characteristics is here based on the filter which may, for example, be described by a sequence of temporally successive signal states.
Further information may, for example, be found in the following documents:    P. Kiesel, M. Bassler, M. Beck and N. Johnson, Appl. Phys. Lett., 2009, 94, p. 41107;    N. Levanon and E. Mozeson, Radar Signals, Wiley, New York, 2004;    R. Turyn and S. Storer, On binary sequences, Proceedings of the American Mathematical Society 12, 394 (1961);    S. Mertens, Exhaustive search for low-autocorrelation binary sequences, Journal of Physics    A: Mathematical and General 29, L473 (1996);    G. E. Coxson and J. C. Russo, E cient exhaustive search for optimal-peak-sidelobe binary codes, IEEE Transactions on Aerospace and Electronic Systems 41, 302 (2005);    P. Borewein, R. Ferguson, and J. Knauer, The merit factor problem, 2000;    C. J. Nunn and G. E. Coxson, Best-known autocorrelation peak sidelobe levels for binary codes of length 71 to 105, IEEE Transactions on Aerospace and Electronic Systems 44, 392 (2008); and    S. K. Shanmugam, C. Mongrédien, J. Nielsen, and G. Lachapelle, Design of short synchronization codes for use in future GNSS system, International Journal of Navigation and Observation 2008 (2008), Article ID 246703.
It is thus the object to find sequences using which the detection of cells may be improved. This object is solved by a device, a method and a computer program for providing information on at least one sequence.